Computer-aided tubing detection

ABSTRACT

A method for detecting tubing in a radiographic image of a patient, executed at least in part by a control logic processor, obtains a radiographic image data for a patient and detects one or more possible tube segments in the image. At least one tubing candidate is formed by growing at least one detected tube segment or merging two or more detected tube segments.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a Continuation of U.S. Ser. No. 12/172,283 entitled“COMPUTER-AIDED TUBING DETECTION”, by Huo, filed Jul. 14, 2008 now U.S.Pat. No. 8,064,675, which claimed priority from U.S. ProvisionalApplication No. 61/024,624 entitled “COMPUTER-AIDED INTERPRETATION OFICU PORTABLE CHEST IMAGES: DETECTION OF ENDO-TRACHEAL TUBES” by Huo,filed on Jan. 30, 2008, and from U.S. patent application Ser. No.11/942,021 entitled “IMAGE ANALYSIS OF TUBE TIP POSITIONING” by Huo,filed Nov. 19, 2007. All of the above are incorporated herein byreference in their entirety.

FIELD OF THE INVENTION

This invention generally relates to processing of diagnostic images andmore particularly to processing that is performed in order to identifythe position of an internal tube positioned within the patient.

BACKGROUND OF THE INVENTION

Portable X-ray radiographs are widely used in the Intensive Care Unit(ICU) for indicating significant or unexpected conditions requiringimmediate changes in patient management. A single diagnostic image mayshow a condition that is related to treatment procedures, such as acollapsed lung or the proper or improper placement of tubing within thepatient. A succession of diagnostic images, taken over a time period,may help to show the progress of a patient's condition and help todirect ICU treatment accordingly.

While portable radiography has advantages for ready accessibility,however, there are some difficulties that limit the accuracy andusefulness of diagnostic images obtained in the ICU. Differences inimage quality from one image to the next can be significant, owing todifferences in exposure settings, patient and apparatus positioning,scattering, and grid application. Even for successive images obtainedfrom the same patient over a short treatment period, there can besubstantial image quality differences between two or more images thatcomplicate or even prevent effective comparison between them, thusconstraining the ability of the clinician to detect subtle changes thatcan be highly significant.

An issue for patient care management relates to the ability to detectthe proper positioning of tubing that has been inserted into thepatient. This tubing includes, for example, endotracheal (ET) tubes,feeding tubes (FTs), and nasogastric tubes (NGTs), among others. Propertube positioning can help to ensure delivery or disposal of liquids andair/gases to and from the patient during treatment. Improper tubepositioning can cause patient discomfort or can render a treatmentineffective. In particular, because of poor image quality in portableanterior-posterior (AP) X-ray images, it is often difficult for aclinician to visually detect, with sufficient certainty, the position ofthe tube tip. Thus, there is a need for a diagnostic imaging method thathelps to identify tubing and tube tip position.

SUMMARY OF THE INVENTION

It is an object of the present invention to address the need forimprovements in automatic detection of tubing and tube tips. With thisobject in mind, the present invention provides a method for detectingtubing in a radiographic image of a patient, executed at least in partby a control logic processor, comprising: obtaining radiographic imagedata for a patient; detecting one or more possible tube segments in theimage; and forming at least one tubing candidate by extending at leastone detected tube segment or by merging two or more detected tubesegments.

It is a feature of the present invention that it traces tubing contourby imaging techniques that grow outwards from an initial detectedposition of a possible tube segment.

The present invention adapts to different imaging apparatus andequipment, so that images taken at different times or on differentimaging systems can be processed and compared.

These and other objects, features, and advantages of the presentinvention will become apparent to those skilled in the art upon areading of the following detailed description when taken in conjunctionwith the drawings wherein there is shown and described an illustrativeembodiment of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims particularly pointing outand distinctly claiming the subject matter of the present invention, itis believed that the invention will be better understood from thefollowing description when taken in conjunction with the accompanyingdrawings.

FIG. 1 is a logic flow diagram showing a basic sequence for tube and tipdetection in embodiments of the present invention.

FIG. 2A shows an example of an x-ray image obtained on a portableradiography system.

FIG. 2B is an image of a mask used for locating lung structures.

FIG. 2C is an image of a mask used for locating spine structure.

FIG. 3 is a logic flow diagram showing a process for obtaining thresholdimages in one embodiment.

FIG. 4A shows an example of a matrix used for enhanced tube patternfeature template processing of an image.

FIG. 4B shows an example FIG. representing the template data stored inan enhanced tube pattern feature template.

FIGS. 4C and 4D show enhanced tube pattern feature templates withorientation at other than vertical angles.

FIG. 4E shows a region of interest (ROI) of the larger image.

FIG. 4F shows the results of enhanced tube pattern feature templateprocessing on the ROI of FIG. 4E.

FIG. 5A shows a 5×6 kernel used for edge detection as a gradient featuretemplate in one embodiment.

FIG. 5B shows an example gray scale image representing the gradientfeature template corresponding to FIG. 5A.

FIG. 5C shows a 5×6 kernel used for edge detection as a gradient featuretemplate in an alternate embodiment, for detecting the opposite edgefrom that detected by the template shown in FIG. 5A.

FIG. 5D shows an example gray scale image representing the gradientfeature template corresponding to FIG. 5C.

FIG. 5E shows a gradient feature template at a non-vertical angle.

FIG. 5F shows an example FIG. representing the template data storedusing a gradient feature template with a non-vertical template angle, asin FIG. 5E.

FIG. 5G shows one example of the results of gradient feature templateprocessing on the ROI.

FIG. 6 is a block diagram showing combination of the feature images inorder to obtain an image for analysis.

FIG. 7 is a logic flow diagram showing a sequence for possible tubesegment detection.

FIG. 8A shows an enhanced tube-pattern-feature template-processed image.

FIG. 8B shows a threshold image obtained by processing the enhancedtube-pattern-feature template-processed image of FIG. 6A.

FIG. 9 is a logic flow diagram for the processing needed for tubeenhancement and merging.

FIG. 10 shows a representative ROI image and a sequence of enhancedtube-pattern feature images showing the progress of a tubing-growthalgorithm.

FIGS. 11A, 11B, and 11C show enhanced original ROI, tube segmentdetection before classification, and final tubing candidate detectionsafter the classification step (false-positive removal step) for tubingdetection as accomplished using an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

It is to be understood that elements not specifically shown or describedmay take various forms well known to those skilled in the art.

Reference is made to commonly assigned U.S. patent application Ser. No.11/644/858 entitled “COMPUTER AIDED TUBE AND TIP DETECTION” by Huo,provisionally filed Nov. 21, 2006 and perfected Dec. 22, 2006,incorporated herein by reference.

The present invention provides a method for automated detection oftubing and tube tips from a radiographic image of a patient. The methodof the present invention can be executed, at least in part, by a controllogic processor, such as a computer, microprocessor, or other dedicatedlogic processing apparatus that has a processor-accessible memory forstoring radiographic image data and that is associated with a displayapparatus for display of a processed image having detected tubing. Themethod detects an initial set of one or more possible tube segmentsusing different feature images of as many as two different types andutilizes growth and merging operations to form tubing candidates in anobtained image. In the description that follows, the method of thepresent invention is directed to detection and display of the ET tubeand tip. Thus, for example, specific anatomy relevant for ET positioningis noted. However, it should be observed that similar steps, withappropriate anatomy used for reference, would apply for detection ofother tubing types.

ET tubing is typically fabricated to have a radio-opaque strip thatmakes it easier to identify in the radiographic image. References in thefollowing description to left and right “edge” refer to detection of theedge of the corresponding line that is generated from this radio-opaquestrip, rather than to detection of the physical edge of the tubingitself.

FIG. 1 is a flow diagram of a detection method 100 showing a basicsequence for tube and tip detection in embodiments of the presentinvention. In an obtain image step S110, the diagnostic image data forthe patient is obtained, such as from a digital radiography (DR or CR)system or from a scanner, for example. An optional image processing stepS120 can be helpful for performing any necessary cleanup and noiseremoval that might be helpful. This processing can include any suitablemethod for pre-processing of the image data, including histogramequalization to enhance the contrast of the grayscale image bytransforming values using Contrast-Limited Adaptive HistogramEqualization (CLAHE), described in commonly assigned U.S. patentapplication Ser. No. 11/644/858, entitled “COMPUTER AIDED TUBE AND TIPDETECTION” by Huo et al.

An optional ROI detection step S130 follows, in which key anatomicalstructures within the Region Of Interest (ROI) for the tubing type areidentified. This step, although optional, helps to reduce computationtime by isolating the area of the image most likely to include thetubing of interest. For ET tube detection, ROI detection identifies thelung and spine regions, since the image of the ET tube is usuallycentered about the spine. The tip of the ET tube is generally about 3-5cm above the carina, located between the two primary bronchi at the siteof the tracheal bifurcation.

By way of example, FIG. 2A shows a chest x-ray obtained at a portableradiographic imaging system. FIG. 2B shows a mask, here a simplepolygon, that is used to coarsely identify the lung area. FIG. 2C thenshows a mask that is used to further isolate the spinal chord. Thecombination of these masks can then be used to help locate the ROI.

Feature Images Generation

In an embodiment of the present invention, a feature images generationstep S140 is executed on the ROI or on some portion or all of the fullimage (FIG. 1) for detecting possible tube segments. Feature images areenhanced template-processed images that are formed by processing imagedata content using one or more templates. In one embodiment, as many asthree feature images are generated as template-processed images usingangle-specific templates: (i) an enhanced tube-pattern featuretemplate-processed image 40 obtained using an enhanced tube-patternfeature template; and (ii) two gradient or edge images obtained usinggradient feature templates.

Using these three feature images with a properly defined ROI provides atubing detection utility with an improved likelihood of success and witha lower false-positive error rate when compared against earlierautomated techniques. Alternate embodiments use one or more featureimages generated using either or both enhanced tube-pattern featuretemplate (i) and gradient feature templates (ii).

Centered pattern enhancement tools are used in applications where localenhancement of an image along a line is needed. One type of centeredpattern enhancement tool uses a Haar-like template or, more generally,an enhanced tube-pattern feature template that applies Haar waveletprocessing, well known to those skilled in the image analysis arts. Inmore general image analysis applications, Haar wavelet processingprovides a number of image processing tools, with various functions thatinclude image information encoding and pattern and feature recognition,for example. Embodiments of the present invention adapt techniquessimilar to those used in Haar wavelet processing as centered patternenhancement tools for enhancing tube-pattern features that can beutilized along with other imaging utilities for detecting possible tubesegments. It can be observed that enhanced tube-pattern featuretemplate-processed image 40 processing, with advantages for detail andpattern recognition, enhances different characteristics of the imagethan does image processing for enhancing edge gradients. Embodiments ofthe present invention can take advantage of either type of processing orof both types of enhanced template-processed images, with both patternand gradient enhancement, combining the results of image processing foreach type of image in order to obtain composite image data that can besuccessfully processed for tubing detection.

The logic flow diagram of FIG. 3 shows a basic sequence for generatingfeature images in feature images generation step S140. A directiondetermination step S141 determines the direction of the spine or othersurrounding anatomical structures and, based on this direction,determines a suitable template direction for the specified ROI. Atemplate determination step S142 then identifies a suitable template. Atleast one of the optional enhanced tube-pattern feature templateprocessing step S143 or optional gradient feature template processingstep S144 follows, employing the direction information that is obtainedearlier to generate suitable centered pattern enhancement and/or edgetemplate processed images. A thresholding step S145 then provides thecentered pattern and/or edge feature images as binary images for thenext stage in processing.

An enhanced tube-pattern feature template-processed image 40 (listed asimage (i) earlier) is generated by applying an enhanced tube-patternfeature template to the raw ROI or to an enhanced version of the raw ROIthat was obtained in step S130 (FIG. 1). By way of example, FIG. 4Ashows an enhanced tube pattern feature template as a matrix of h₁×h₂size, where h₁=h₂=4. The angular orientation for kernel rotationparameter θ in this example is 0 degrees. Note that the number of thecolumns with values 1 and −1 in the kernel used for enhanced tubepattern feature template processing can be variable, determined based onthe size and width of the tubing or other structure of interest.

In one exemplary embodiment, a set of 8 enhanced tube-pattern featuretemplates of the same kernel size and of different angles (θ_(k)=kπ/8,k=−3, −2, −1, 0, 1, 2, 3, 4, h₁=h₂=20) are used for ET tube detection.FIG. 4B shows the image of one template (θ=0, h₁=20, h₂=20), where thewhite area represents 1 and the black area represents −1. The rest ofthe enhanced tube-pattern feature templates are rotated by the angleθ_(k). FIGS. 4C and 4D show enhanced tube-pattern feature templates atother than vertical angles.

In an embodiment, one enhanced tube-pattern feature template-processedimage from the set is selected for each ROI. For ET tubing detection,the template whose angle θ is most similar to the direction of thedetected spine for a given image is generally preferred. For the givenexample in FIG. 2A, FIG. 4E shows the selected ROI. FIG. 4F shows thecorresponding enhanced tube-pattern feature template-processed imagethat results from this processing. As can be seen from this example,tubing structures that are barely noticeable in the original image aremuch more pronounced in the image that is generated using the enhancedtube-pattern feature template.

As noted earlier, an embodiment of the present invention identifiestubing structures by combining the results obtained from the optionalenhanced tube-pattern feature template processing (part (i), above) withthose obtained using optional gradient feature template image processing(part (ii), above). For generating optional gradient or edge images,another type of template is applied. The example of FIG. 5A shows agradient feature template that can be used for processing that detectsthe left edge of the radio-opaque tubing line, as shown in FIG. 5B. Theexample of FIG. 5C then shows a gradient feature template that can beused for processing that detects the right edge of the radio-opaquetubing line, as shown in FIG. 5D. For both of these templates, the size(h₁×h₂) of the kernel is a factor of the size and width of the tubingline of interest. Here, the kernel size of h₁=5, h₂=6 is used. The angleθ (θ_(k)=kπ/8, k=−3, −2, −1, 0, 1, 2, 3, 4) for generating the gradientor edge image is determined based on the direction of the detectedspine. Again, the template whose angle θ is most similar to thedirection of the detected spine for a given image is preferred.

FIG. 5E shows a gradient feature template at a non-vertical angle, asshown in FIG. 5F. The example of FIG. 5G shows gradient detection usingan angle θ=π/4 and h₁=13, h₂=20.

FIG. 6 shows graphically how as many as three images obtained in steps(i) and (ii) described earlier are used for tubing detection. Acomposite image set is formed from enhanced tube-pattern featuretemplate-processed image 40, left-edge gradient image 60 l, andright-edge gradient image 60 r. In this way, results fromcentered-pattern enhancement and gradient image processing areeffectively added to each other, enabling the resulting image data toshow likely tubing structures in a manner that is more sensitive androbust than other techniques show.

A thresholding step S145 (FIG. 3) assigns a binary value to each pointin either enhanced tube-pattern feature template-processed image 40 orgradient image 60 l or 60 r, or in both types of images according to apredetermined threshold value. In one embodiment, the threshold for thispurpose is set at 1.7% of the maximum value. All pixel values below thisthreshold are assigned one (dark) value; all pixel values at or abovethe threshold are assigned another value. The composite image set ofFIG. 6 can be generated before or after the thresholds applied to theenhanced tube pattern feature template-processed image 40 and gradientimages 60 l, 60 r.

Possible Tube Segment Detection

In detection of possible tube segments, the paired left and right edgeson the gradient images correspond to the left and right edges of theradio-opaque stripe in the tube and are thus considered as identifyingpossible tube segments. The search proceeds by checking along each lineof pixels between paired left and right edges for the point that has themaximum centered-pattern enhancement value. The points with maximumvalues in between the paired left and right edges correspond to centerpoints of the tube segment.

Referring back to the logic flow diagram of FIG. 1, once the featureimage, whether either or both enhanced tube-pattern featuretemplate-processed image 40 and gradient images 60 l and 60 r, isobtained in step S140, a possible tube segment detection step S150 canbe carried out. The more detailed block diagram of FIG. 7 shows thesequence for possible tube segment detection step S150 in oneembodiment.

In a generate feature mask step S155, a feature mask is formed. To dothis in one exemplary embodiment:

1.) For each line of the image, a local maximum is obtained. This can bea maximum on each of the left- and right-edge images, where these havebeen generated.

2.) The local maximum of the enhanced tube-pattern featuretemplate-processed image, lying between the edges detected in the leftand right images, is determined.

3.) Possible tube segments are identified using a correspondence ofenhanced tube-pattern feature template-processed image and gradientfeature template processing. To do this processing, a pair of suitableleft- and right-edge image points on a line that are appropriatelydistanced from each other are identified. Pixels that lie between thetwo edges on the line are identified and can be grouped to definepossible tube segments for tube detection, based on a detection sequencethat checks for the maximum centered-pattern enhancement value lyingbetween these points.

4.) Perform a closing operation on the mask, such as using a 5×1 kerneloperation, for example.

In an alternate embodiment, possible tube segments are identified usingseparate processing results from only the enhanced tube-pattern featuretemplate or using only one or two of the gradient feature templates.Combining results from these individual feature templates, however, mayhave particular advantages for identifying possible tube segments forsome type of images.

Continuing with the sequence of FIG. 7, once possible tube segments areidentified, a region labeling and selection step S156 is then executedon feature mask results. Region labeling is a binarization process thathelps to identify tubing characteristics for possible tube segments orpatches that meet or exceed a minimum threshold. This step examines eachpart identified using the feature mask. Region labeling connects thepoints of detected possible tube segments using a suitable technique,such as four-point connectivity checking, for example. Where the widthis 0, a pair of edges has not been detected. The corresponding possibletube segment is rejected. Any detected initial possible tube segmentsare then stored for further processing in a storage step S158.

FIGS. 8A and 8B compare an edge image to the resulting threshold imageobtained using initial possible tube segment detection according to thepresent invention.

Tube Enhancement and Merging to form Tubing Candidates

Referring back to FIG. 1, a tube enhancement and merging step S160follows, in which, to form a primary tubing candidate, a possible tubestructure is extended and merged with other candidate sections. Thelogic flow diagram of FIG. 9 lists steps that are used for tubeenhancement and merging step S160 in one embodiment. FIG. 10 then showsa series of images for tube enhancement and merging step S160 in ET tubedetection for an ROI 30. In FIG. 10, tube enhancement results are shownrelative to an enhanced tube pattern feature template-processed image40. The tubing growth that is shown as a tube segment 42 with tubinggrowth indicated at 44 and 46 extends upward (in terms of theorientation shown). The tubing growth shown at 48 and 50 begins toextend downward.

In one embodiment, tube enhancement involves linear line or curve(second and third polynomial) fitting of each detected tube segment forforming a tubing candidate, using methods familiar to those skilled inmathematical curve-fitting techniques. These initial possible tubesegments are usually in the form of broken lines or patches. As shown inthe sequence of FIG. 10, enhancement starts from both ends of the brokenlines, extending along the direction of the fitted line or curve. Thetube enhancement algorithm first searches for a predefined number ofpoints along the fitted line or curve at both ends. If a fraction ofthese points meet a minimum value set on the enhanced tube patternfeature template-processed image 40, these points become part of theinitial tube, and another set of points along the fitted line are thenexamined. The search continues until a new set of points fails to meetthe criteria.

In the logic flow of FIG. 9, an initialization step S161 begins byobtaining one of the initial possible tube segments that had beenidentified in step S150. For each possible tube segment, the points ineach row of pixels that have a maximum centered pattern enhancementvalue are identified and used as the tube center point set S. Thecorresponding value of these center points in the centered patternenhancement from enhanced tube pattern feature template-processed image40 is denoted as set V.

A fitting step S162 is then executed in order to fit the possible tubesegment to a 3^(rd)-order polynomial curve, such as a curve conformingto enhanced tube pattern feature template-processed image 40 (FIG. 10).This sequence is carried out as follows in one embodiment:

1) The x,y position of each point in set S is fitted to a 3^(rd)-orderpolynomial fitting curve in a fitting step S162.

2) The possible tube segment is extended along this fitting curve in anextension step S164. For this step, the possible tube segment isextended along the fitting curve in increments. In one embodiment, theincrement is the lesser of 30 pixels or ⅓ of the identified initialpossible tube segment length. The extended point set is labeled E.

3) Check for completion of growth or continue. A test step S166 is thenexecuted in order to determine whether or not growth can continue fromthe extended tube segment. For test step S166, a binary sequence Seq forthe enhanced tube segment is generated and used as follows:

3a) Point set E's corresponding value in the enhanced tube patternfeature template-processed image 40 is denoted as V_(E). For any point Qin set E, the following assignment of a binary value is made:

${{if}\mspace{14mu}\frac{V_{e}}{{Avg}(V)}} > {0.05\mspace{14mu}{set}\mspace{14mu} Q\mspace{14mu}{to}\mspace{14mu} 1}$else, set  Q  to  0.

This creates a binary sequence Seq of 1s and 0s corresponding to thiscentered pattern enhancement content for set E.

3b) If Seq begins with 1 for set E, such as

$\underset{{t_{k\mspace{50mu}}t_{k}^{\prime}}\mspace{214mu}}{11111000001111001}$(where t_(k) denotes the start position of the binary sequence andt′_(k) denotes the position before first zero), then add the pointsbetween t_(k) and t′_(k) to set S. Reassign t_(k)=t′_(k). Return to thebeginning of step 3).

3c) If Seq begins with 0, such as

$\underset{{t_{k}t_{k}^{\prime}}\mspace{175mu}}{00111110000011001\;}$(where t_(k) denotes the start position of the binary sequence. t′_(k)denotes the position before the first 0 that occurs after 1), if int_(k)<t≦t′_(k), the total number of zeros is greater than a giventhreshold T_(N), stop the enhancement process in a termination stepS168. Otherwise, add the points in t_(k)<t≦t′_(k) to set S, assignt_(k)=t′_(k), and return to the beginning of step 3). Threshold T_(N) isassigned as 16 in one embodiment.

Merging of tube segments to form a tubing candidate can be performed ina fairly straightforward manner. After fitting two tube segments havingoverlapping rows of pixels, the mean fitting difference between the twofitting curves is computed. Where this mean value is smaller than anempirically determined constant c, the two tube segments can be merged.

Two nearby tube segments may not have any overlap rows. In such a case,after fitting the two tube segments, if the mean fitting difference inthose rows between two tube segments is smaller than an empiricallydetermined constant c and the centered pattern enhancement mean value inthese rows exceeds a second predetermined threshold value, the tubesegments can be merged together.

Note that the sequence just described for tube enhancement and mergingshows one of a number of possible embodiments. Methods that allowincremental growth and continual testing, such as the sequence justdescribed, are advantaged over other possible methods for linkingidentified possible tube segments to form a tubing candidate.

Feature Extraction for Removing False Positives

Referring again to the flow chart of FIG. 1, a false-positive removalstep S170 follows tube enhancement and merging step S160. For eachidentified tubing candidate, features such as the width, variation inwidth, length of the detected tubing candidate, and tube positionrelative to other anatomy structures such as lung and spine arecalculated. In addition, statistics from the analysis of the detectedtube lines, such as mean curvature, standard deviation, and thevariation in curvature of the fitted line can be calculated. In oneembodiment, a total of seven features are calculated for each tubingcandidate. Tip position and tube width standard deviation can also beused to help detect and eliminate false positive tubing candidates.

Among features that have been found to be particularly useful for ETtube classification are tube width, 1-degree fitting error, 3-degreefitting error, tube/spine angle difference, mean value at tube, tubewidth and position, and tube percentage in initial regions, that is,percentage of pixels initially determined to be part of a tubingstructure. Other features could similarly be extracted and used forfalse-positive removal. In one embodiment, linear and quadraticdiscriminant analysis methods (QDA) are employed to analyze thesefeatures for differentiating true-positive from false-positive tubingdetections.

FIGS. 11A through 11C show the original image, results of initial tubingcandidate detection, and final results for an ET tube detection sequencein one embodiment of the present invention. In FIG. 11B, a number offalse positive tubing candidates can be identified, labeled FP1, FP2,FP3, FP4, and FP5. It can be seen from this example that characteristicssuch as curvature and overall position can be useful for eliminating apercentage of these false positives. At least significant portions offalse positives FP1, FP2, and FP3 are poorly positioned, which can bedetected in a straightforward manner using the detectable location ofspine and other anatomical structures. Moreover, false positives FP1,FP2, and FP3 also exhibit significant amounts of curvature, more thanwould be anticipated for ET tubing. False positive FP1 shows variationin curvature, making it a particularly unlikely candidate. Falsepositives FP4 and FP5 have better position and reduced curvature, butfail for other reasons. False positive FP4 appears to be too short andis not connected with other tubing that leads further upward ordownward. False positive FP5 has excessive length, extending well pastthe carina in this example. For these reasons, each of false positivesFP1-FP5 can be removed from consideration.

Once false-positive tubing candidates have been identified anddiscarded, the image of the detected tubing can be highlighted in thedisplay that is presented to the viewer of the x-ray images, such as ona high-resolution display screen. FIG. 11C shows the successfuldetection of ET tubing for this example. The section of ET tubing forthis patient, detected following the classification of false positivesin steps just described, appears to have suitable condition, thickness,curvature, and other features. Highlighting of the detected tubing canbe performed in a display highlighting step S180. As part of this step,color can be used to outline or otherwise highlight the display oftubing obtained using the steps shown in FIG. 1.

The method of the present invention has been shown to yield favorableresults for tube and tip detection over other methods. Improved tubingdiscrimination with this method also results in a reduced number offalse-positive readings. With one sample set of test images, quadraticdiscriminant analysis for false positive detection, applied using thegeneral sequence described, obtained a reduction in the number offalse-positives without measurable sensitivity loss. Results showed asensitivity of 92% at 1.5. FPs/image. Earlier methods had achievedapproximately 80% sensitivity at the same relative number of falsepositive per image.

The invention has been described in detail with particular reference tocertain preferred embodiments thereof, but it will be understood thatvariations and modifications can be effected within the scope of theinvention as described above, and as noted in the appended claims, by aperson of ordinary skill in the art without departing from the scope ofthe invention. For example, as noted earlier, any of a number ofdifferent methods could be used for ROI detection, including the use ofearlier tube detection results for the same patient, for example. Asingle edge image could be obtained and analyzed and used for storingboth left- and right-edge content.

Thus, what is provided is a method for enhancing diagnostic images inorder to detect the position of tubes positioned within the patient.

PARTS LIST

-   30. ROI-   40. Enhanced tube pattern feature template-processed image-   42. Tube segment-   44, 46, 48, 50. Tubing growth-   60 r, 60 l. Gradient image-   100. Detection method-   S110. Obtain image step-   S120. Image processing step-   S130. ROI detection step-   S140. Feature images generation step-   S141. Direction determination step-   S142. Template determination step-   S143. Enhanced tube pattern feature template processing step-   S144. Gradient feature template processing step-   S145. Thresholding step-   S150. Candidate detection step-   S155. Feature mask generation step-   S156. Region labeling and selection step-   S158. Storage step-   S160. Tube enhancement and merging step-   S161. Initialization step-   S162. Fitting step-   S164. Extension step-   S166. Test step-   S168. Termination step-   S170. False positive removal step-   S180. Display highlighting step-   FP1, FP2, FP3, FP4, FP5. False positives

1. A method for detecting tubing in a radiographic image of a patient,comprising: obtaining a radiographic image for a patient; detecting atleast one tube segment in the image; and forming at least one tubingcandidate by growing the at least one detected tube segment byidentifying a group of consecutive pixels along a fitted curve of thedetected tube segment.
 2. The method of claim 1 further comprisingextracting one or more features to characterize the at least one tubingcandidate.
 3. The method of claim 2 further comprising analyzingextracted features to remove one or more false-positive tubingcandidates.
 4. The method of claim 3 wherein analyzing extractedfeatures to remove one or more false-positive tubing candidatescomprises analyzing one or more of curvature, length, position relativeto patient anatomy, detected tip position, and tube width standarddeviation using quadratic discriminate analysis.
 5. The method of claim2 wherein extracting one or more features comprises identifying afeature taken from the group comprised of tubing candidate width,1-degree and 3-degree errors from curve-fitting of the at least onetubing candidate, tube/spine angle difference, mean value at the tubingcandidate, angular position of the tubing candidate, percentage oftubing candidate pixels within a fitted curve, length of the detectedtubing candidate, tubing candidate position relative to one or moreanatomy structures, mean curvature of the tubing candidate, and thevariation in curvature of the tubing candidate.
 6. The method of claim 1further comprising identifying a region of interest in the image fordetection of tube segments.
 7. The method of claim 1 wherein detectingat least one tube segment comprises: forming at least onetemplate-processed image by processing at least a portion of theradiographic image data using one or more gradient feature templates orusing an enhanced tube pattern feature template; and applying an imagethresholding algorithm to the at least one template-processed image,forming a thresholded image thereby.
 8. The method of claim 7 whereinthe one or more gradient feature templates are selected according to anangular orientation of the patient's spine.
 9. The method of claim 7wherein the enhanced tube pattern feature template is selected accordingto an angular orientation.
 10. The method of claim 7 wherein theenhanced tube pattern feature template is a Haar-like template.
 11. Themethod of claim 7 wherein forming at least one template-processed imagefurther comprises treating the image content using thresholding.
 12. Themethod of claim 1 wherein detecting at least one tube segment comprisesusing a gradient feature template to identify one or more edges of thetube segment and using an enhanced tube pattern feature template toidentify a center of the tube segment.
 13. The method of claim 1 furthercomprising performing contrast enhancement on the radiographic image.14. The method of claim 1 further comprising displaying the at least onetubing candidate.
 15. The method of claim 14 wherein displaying the atleast one tubing candidate comprises highlighting the at least onetubing candidate on a display screen.
 16. The method of claim 1 whereingrowing the at least one detected tube segment comprises merging two ormore detected tube segments according to a distance between the two ormore detected tube segments.
 17. The method of claim 1 wherein detectingat least one tube segment comprises processing at least a portion of theradiographic image data using at least one gradient feature template orusing an enhanced tube pattern feature template, forming at least onetemplate-processed image thereby; applying a thresholding algorithm tothe at least one template-processed image; and identifying an edge orcenter pattern within the at least one template-processed image.
 18. Themethod of claim 1 wherein growing is based on the analysis ofthresholded template-processed images.